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Inside AlphaFold 3: A Technical View Into the New Version of Google DeepMind’s BioScience Model
Artificial Intelligence   Latest   Machine Learning

Inside AlphaFold 3: A Technical View Into the New Version of Google DeepMind’s BioScience Model

Last Updated on May 14, 2024 by Editorial Team

Author(s): Jesus Rodriguez

Originally published on Towards AI.

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AI for science is one of my favorite forms of AI 😊. You can argue that the real test for AGI is when it’s able to create new science. No company in the world has been pushing the boundaries of AI for science like Google DeepMind. Among the scientific achievements of DeepMind models, none has achieved more notoriety than AlphaFold, the model that was able to predict protein structures from a sequence of amino acids. A few days ago, DeepMind published details about AlphaFold 3, which expands its prediction capabilities beyond just proteins to a broad spectrum of biomolecules.

Our understanding of life’s molecules is a core foundation of our understanding of biological life and certainly the cornerstone of drug discovery. AlphaFold 3 is able to predict the structure of large molecular structures such as proteins, RNA, DNA, or even small molecules such as ligands. Even more impressive is the fact that AlphaFold 3 can model the chemical interactions in those molecules which effectively control cell functioning. Starting with a list of molecules, AlphaFold 3 is able to generate a 3D structure that clearly visualizes its joint 3D structure, revealing its intricacies and interactions.

The Functionality

Google DeepMind’s AlphaFold 3 showcases advanced capabilities originating from its innovative architecture and extensive training, which now encompasses the entirety of biological molecules. At the heart of AlphaFold 3 is the enhanced Evoformer module, which significantly contributed to the impressive performance of its predecessor, AlphaFold 2. After initial data processing, AlphaFold 3 utilizes a diffusion network, similar to those used in AI-driven image generation, to assemble its predictions. This process begins with a generalized atomic cloud which iteratively refines into a precise and accurate molecular structure.

In terms of predictive accuracy regarding molecular interactions, AlphaFold 3 surpasses all existing models. Its unified approach allows it to compute complex molecular structures holistically, facilitating a deeper integration of scientific insights.

The Architecture

The architectural framework of AlphaFold 3 (AF3) has evolved from AlphaFold 2, maintaining a large central section that develops a pairwise depiction of the chemical complex. This is then utilized by a Structure Module to determine exact atomic coordinates. However, substantial changes have been made across various components to better support a diverse array of chemical entities and to build upon insights gained from the performance tweaks applied to AlphaFold 2. The main trunk now features a scaled-down MSA processing unit, simplifying the embedding block. The updated model, dubbed the “Pairformer,” now focuses solely on pair and single representations, sidestepping the MSA representation entirely.

Image Credit: Google DeepMind

The diffusion module, a new addition, directly handles raw atom coordinates, simplifying the process without the need for complex rotational adjustments or equivariance.

Image Credit: Google DeepMind

AlphaFold Server

The launch of the AlphaFold Server by Google DeepMind represents a significant leap forward in the field of molecular biology. As the most accurate tool available for modeling protein interactions within cells, this freely accessible server offers a vital resource for non-commercial scientific research worldwide. With a user-friendly interface, the server enables biologists to quickly model complex structures involving proteins, DNA, RNA, and various other molecules.

The AlphaFold Server not only accelerates the formulation and testing of new scientific hypotheses but also enhances the overall pace of innovation by streamlining research workflows. It democratizes access to cutting-edge predictions, empowering researchers regardless of their computational means or their familiarity with machine learning.

Traditionally, experimental prediction of protein structures could extend over the duration of a PhD and cost a significant sum. In contrast, AlphaFold 2, the predecessor model, has been utilized to predict structures on a scale that would have required unimaginable human and financial resources in traditional structural biology.

AF3 represents a major breakthrough in AI for biosciences. The model does nothing short of bringing the molecular world into high definision opening new avenues for understanding the intricacies of biological functions.

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Published via Towards AI

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